Loan Repayment Prediction System: A Full Stack Machine Learning Approach for Financial Risk Assessment

This paper presents the design, development, and deployment of a full-stack machine learning system for predicting loan repayment default, aimed at enhancing financial risk assessment. Utilizing the Home Credit Default Risk dataset from Kaggle, this study conducts a comparative analysis of three machine learning models: Logistic Regression, Random Forest, and XGBoost. The system architecture integrates a React frontend for user data collection and a Python Flask backend API that serves a serialized (.pkl) XGBoost model for real-time predictions. A key contribution of this work is the integration of SHapley Additive explanations (SHAP) to address the "black box" nature of complex models, providing transparent, feature-level interpretability for each prediction. The experimental results demonstrate that the XGBoost model achieves superior predictive performance, with a ROC-AUC score of 0.778. The application of SHAP successfully elucidates the key drivers behind individual predictions, making the model's decisions transparent and actionable for financial stakeholders. This project provides a practical blueprint for building and deploying interpretable, high-performance machine learning solutions that bridge the gap between theoretical modeling and real-world financial decision-making, thereby promoting more responsible and data-driven lending practices.